from llama_index.core import SimpleDirectoryReader
from llama_index.core.node_parser import SimpleNodeParser
from llama_index.core import  GPTVectorStoreIndex,VectorStoreIndex
from llama_index.llms import openai_like
from llama_index.core import Settings
from llama_index.llms.ollama import Ollama
from llama_index.embeddings.huggingface import HuggingFaceEmbedding  # HuggingFaceEmbedding:用于将文本转换为词向量
from llama_index.llms.huggingface import HuggingFaceLLM  # HuggingFaceLLM：用于运行Hugging Face的预训练语言模型
from llama_index.core import Settings,SimpleDirectoryReader,VectorStoreIndex
import chromadb
from llama_index.embeddings.dashscope import DashScopeEmbedding
from llama_index.vector_stores.chroma import ChromaVectorStore
from llama_index.core import StorageContext, load_index_from_storage
from llama_index.llms.deepseek  import DeepSeek
from llama_index.embeddings.fastembed import FastEmbedEmbedding
from llama_index.core.text_splitter import CodeSplitter
from llama_index.llms.openai import OpenAI
from llama_index.packs.code_hierarchy import (
    CodeHierarchyAgentPack,
    CodeHierarchyNodeParser,
)
    # 连接Chroma数据库


llm = DeepSeek(model="deepseek-chat", api_key="sk-605e60a1301040759a821b6b677556fb")
Settings.llm = llm
embed_model = FastEmbedEmbedding(model_name="BAAI/bge-small-en-v1.5")
Settings.embed_model = embed_model


documents = SimpleDirectoryReader('python',file_metadata=lambda x: {"filepath": x},).load_data()



split_nodes = CodeHierarchyNodeParser(
    language="python",
    # You can further parameterize the CodeSplitter to split the code
    # into "chunks" that match your context window size using
    # chunck_lines and max_chars parameters, here we just use the defaults
    code_splitter=CodeSplitter(
        language="python", max_chars=1000, chunk_lines=10
    ),
).get_nodes_from_documents(documents)

pack = CodeHierarchyAgentPack(split_nodes=split_nodes, llm=llm)

output=pack.run(
    "解释 main 函数的功能 "
)
print(output)